With the rapid development of information technology, traditional neural networks used as feature extraction networks can improve the network’s fitting ability but may lose information for small object detection, resulting in low accuracy. In this paper, the image acquisition device and Unet detection model were built independently. The algorithm accurately detects the sensor chip overflow using image processing techniques with OpenCV. Finally, the detected images are presented using PyQt.Experimental results show that the improved Unet-glue algorithm achieves better segmentation accuracy for chip overflow. It also demonstrates strong robustness and practicality in the field of small object defect detection.
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